In a recurrent artificial neural network, the units active in an attractor state typically reach their maximum activity value while the others are quiescent. In contrast, recordings of cortical cell activity in vivo r...
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In a recurrent artificial neural network, the units active in an attractor state typically reach their maximum activity value while the others are quiescent. In contrast, recordings of cortical cell activity in vivo rarely reveal cells firing at their maximum rate. This discrepancy has been one of the main arguments against using attractor networks as models of cortical associative memory. In this study we show that low-rate sustained after-activity can be obtained in a simulated network of mutually exciting pyramidal cells. This is achieved by assuming that the synapses in the network are of a saturating type. When the application of a monoamine neuromodulator is simulated, after-activity with firing rates around 60?s?1 can be produced. The firing pattern of the network was found to be similar to that of the experimentally most comparable system, the disinhibited hippocampal slice. The results obtained are robust against simulated biological variation and background noise.
作者：
LANSNER, ALILJENSTROM, HSANS
Studies of Artificial Neural Systems Department of Numerical Analysis and Computing Science Royal Institute of Technology S-100 44 Stockholm Sweden
The recent developments in computer capacity and algorithms, together with a tremendous growth of data in neuroscience have dramatically improved the possibilities of modeling and simulating certain brain structures a...
The recent developments in computer capacity and algorithms, together with a tremendous growth of data in neuroscience have dramatically improved the possibilities of modeling and simulating certain brain structures and activities with a considerable degree of realism. Although there is still a long way to go, some claim that we will one day be able to create artificial ''brains'' with similar capacity to the human brain, perhaps even surpassing it. Here we focus on these perspectives, discussing the potentials and limitations of today's computer models, and how far they might be able to take us.
We present an algorithm to compute the Hilbert series of a homogeneous ideal in a polynomial ring. From an ideal I, generated by the principal monomials of a Gröbner basis, we compute the Hilbert series \(Hilbk[x...
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Computer simulation of neuronal networks is rapidly becoming accepted as a powerful tool in neuroscience. We illustrate the trends in this field by looking at motor generation and control, with examples from recent mo...
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We investigate the power of threshold circuits of small depth. In particular, we give functions that require exponential size unweighted threshold circuits of depth 3 when we restrict the bottom fanin. We also prove t...
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We show that there are functions computable by linear size boolean circuits of depth k that require superpolynomial size perceptrons of depth k - 1, for k 0, there arefunctions that are computable by circuits of line...
ISBN:
(纸本)0780342674
We show that there are functions computable by linear size boolean circuits of depth k that require superpolynomial size perceptrons of depth k - 1, for k < logn/(6loglogn). This result implies the existence of an oracle A such that [GRAPHICS] and in particular this oracle separates the levels in the PPPH hierarchy. Using the same ideas, we show a lower bound for another function, which makes it possible to strengthen the oracle separation to [GRAPHICS] al scheme is established to study the trade-off between circuit bottom fan-in and circuit size. Based on this scheme, we are able to prove, for example, that for any integer c, there are functions that are computable by circuits of linear size and depth k with bottom fan-in O(log n) but require exponential size for circuits of depth k with bottom fan-in c, and that for any constant epsilon > 0, there arefunctions that are computable by circuits of linear size and depth k with bottom fan-in log n but require superpolynomial size for circuits of depth k with bottom fan-in O(log(1-epsilon) n). A consequence of these results is that the three input read-modes of alternating Turing machines proposed in the literature are all distinct.
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